'Interactive Classification: get more from less by Ilze Birzniece, LV

•

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This document proposes an interactive classification system called InClas that involves human experts to improve automatic classification results. It summarizes that traditional automatic classification methods are not always appropriate, especially in domains with small training data, complex attributes, or when experts do not fully trust automated outputs. InClas detects uncertain and unclassified objects for human experts to review and provides a transparent model that the expert can update. Experimental results show InClas can significantly reduce misclassified objects compared to fully automatic classifiers. The document recommends InClas for domains where human experts are available and attributes are understandable, or when obtaining correct classifications for many objects is important and expert time investment is acceptable.

Technology Education

INTERACTIVE
CLASSIFICATION WITH
INCLAS:
GET MORE FROM LESS

M.sc.ing. ILZE BIRZNIECE

Predictive
analytics
Data
mining

Classification

Artificial
intelligence

Business
intelligence

Machine
learning

DATA
2

CLASSIFICATION


Learning from the past experience
E.g. weather prediction, medical diagnostic, organizing
documents, credit scoring etc.
 Formalized data (attributes, classes)
 Experience (training examples)


No.

Income
Has loan
(Eur/month)

Client
(month)

...

Outcome

1

1200

yes

23

..

Untrustful

2

900

no

50

..

Trustful

1700

yes

2

..

?

..
x


Various methods and tools for classification task

3

WHY NEW APPROACH?
Inappropriateness of automatic classification
methods for every domain where machine
learning techniques could be applied to
 Practical need to help experts in area of curricula
comparison


Experts do not trust fully
automated solutions
Poor
performance

Complex and hard to formalize domain
Small training base
Incompleteness of classifier

4

INTERACTIVE CLASSIFICATION

Automatic
classification

Manual
classification

Interactive
(semiautomatic)
classification
5

INTERACTIVE APPROACH
Interactive approach includes:
 Featuring classifier with ability to detect unclassified
and uncertainly classified objects
•Asking for
the help of
human
•Updating
classifier
•Using
transparent
classifier

6

FEATURES OF INCLAS (INTERACTIVE
CLASSIFICATION SYSTEM)


Dealing with








o
multi-label class membership
o
semi-structured and unstructured data
o
small initial training base
many classes with similar probability to appear o
o
various confidence thresholds

single-label
structured
sufficient
two classes
traditional

Involving expert in order to achieve better results
7

EXPERIMENTAL RESULTS



Number of misclassified objects can be (significantly)
reduced if an interactive classification system is
applied
Medical diagnostics*

Study course comparison
0,06

0,367

0,267

0,366

(Partly)
Correct
Misclassified

0,09

0,85

UnClassified
9

* From Computational Medicine Center's 2007
Medical Natural Language Processing Challenge

VISION
InClaS:

get more from less
knowledge

data

Broadening application areas of InClaS
 Extending current prototype


10

TARGETED ADVERTISING




Lowering advertising costs
Adressing right audience

Setting confidence threshold
 Consulting with expert


11

DOCUMENT (ARTICLE, PICTURE ETC.)
ORGANIZATION




Multiple categories for each object
Limited amount of categorized data

Multi-label classification
 Overall approach for using and
improving weak classifiers


12

RECOMMENDATIONS OF INCLAS
APPLICATION


The use of the interactive classification system is
feasible in areas where:
Human-expert is available
 Problem domain is defined by the attributes which
are comprehensible for the expert




The interactive classification approach is more
appropriate in areas where at least one of the
following statements holds:
It is essential to receive a correct classification for as
much objects as possible, and it is acceptable to
invest the expert’s work and time to achieve it
 It is hard to extract or define domain features
 Only a small initial learning set is available


13

LOOKING FOR COOPERATION

Ilze Birzniece
ilze.birzniece@rtu.lv

Summary of Doctoral Thesis

Development of Interactive Inductive
Learning Based Classification System's
Model

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